Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition
Abstract
Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.
Downloads
References
Altman N, Krzywinski M. The Curse(s) of Dimensionality. Nat Methods. 2018;15(6):399–400. DOI: https://doi.org/10.1038/s41592-018-0019-x
Syarif I. Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization. Emit Int J Eng Technol. 2016 Dec 15;4(2). Available from: https://emitter.pens.ac.id/index.php/emitter/article/view/149 DOI: https://doi.org/10.24003/emitter.v4i2.149
Mohammadi FG, Amini MH, Arabnia HR. Evolutionary Computation, Optimization, and Learning Algorithms for Data Science. Optim Learn Control Interdependent Complex Networks Adv Intell Syst Comput. 2020;1123:37–65. DOI: https://doi.org/10.1007/978-3-030-34094-0_3
LeCun Y, Cortes C. MNIST handwritten digit database. 2010; Available from: http://yann.lecun.com/exdb/mnist/
Arora M. Feature Extraction-Principal Component Analysis. Medium. 2019.
Gharavian D, Sheikhan M, Ghasemi SS. Combined Classification Method for Prosodic Stress Recognition in Farsi Language. Int J Speech Technol. 2018;21(2):333–41. DOI: https://doi.org/10.1007/s10772-018-9508-7
Bäck T, Fogel DB, Michalewicz Z. Evolutionary computation 1: Basic algorithms and operators. CRC press; 2018.
Amstrong M. Stress Is Biggest Threat To Workplace Health. Stress and burnout - Statista. 2016.
Ahmad J, Sajjad M, Rho S, Kwon S il, Lee MY, Baik SW. Determining Speaker Attributes from Stress-affected Speech in Emergency Situations with Hybrid SVM-DNN Architecture. Multimed Tools Appl. 2018;77(4):4883–907. DOI: https://doi.org/10.1007/s11042-016-4041-7
Adi DP, Gumelar AB, Arta Meisa RP. Interlanguage of Automatic Speech Recognition. In: 2019 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE; 2019. p. 88–93. DOI: https://doi.org/10.1109/ISEMANTIC.2019.8884310
Stewart C. Distribution of stress levels at work by those in paid employment and training programs in Scotland in 2017, by gender. State of Health - Statista. 2020.
Holland P, Collins AM. “Whenever I can I push myself to go to work”: a qualitative study of experiences of sickness presenteeism among workers with rheumatoid arthritis. Disabil Rehabil. 2018 Feb;40(4):404–13. DOI: https://doi.org/10.1080/09638288.2016.1258436
Venkataramanan K, Rajamohan HR. Emotion Recognition from Speech. arXiv Prepr arXiv191210458. 2019 Dec;
Ahmad J, Fiaz M, Kwon S, Sodanil M, Vo B, Baik SW. Gender Identification using MFCC for Telephone Applications - A Comparative Study. 2016;3(5).
Deb S, Dandapat S. Classification of Speech Under Stress Using Harmonic Peak to Energy Ratio. Comput Electr Eng. 2016;55:12–23. DOI: https://doi.org/10.1016/j.compeleceng.2016.09.027
Chadha AN, Zaveri MA, Sarvaiya JN. Optimal feature extraction and selection techniques for speech processing: A review. In: 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE; 2016. p. 1669–73. DOI: https://doi.org/10.1109/ICCSP.2016.7754447
Slavich GM, Taylor S, Picard RW. Stress Measurement Using Speech: Recent Advancements, Validation Issues, and Ethical and Privacy Considerations. Stress. 2019;22(4):408–13. DOI: https://doi.org/10.1080/10253890.2019.1584180
Sharma M, Kaur P. A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem. Arch Comput Methods Eng. 2020 Feb; DOI: https://doi.org/10.1007/s11831-020-09412-6
Chen X, Bai R, Qu R, Dong H, Chen J. A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE; 2020. p. 1–8. DOI: https://doi.org/10.1109/CEC48606.2020.9185659
Talbi E-G, Jourdan L, Garcia-Nieto J, Alba E. Comparison of Population based Metaheuristics for Feature Selection: Application to Microarray Data Classification. In: 2008 IEEE/ACS International Conference on Computer Systems and Applications. IEEE; 2008. p. 45–52. DOI: https://doi.org/10.1109/AICCSA.2008.4493515
Chen Y, Miao D, Wang R. A Rough Set Approach to Feature Selection based on Ant Colony Optimization. Pattern Recognit Lett. 2010 Feb;31(3):226–33. DOI: https://doi.org/10.1016/j.patrec.2009.10.013
Rodriguez-Galiano VF, Luque-Espinar JA, Chica-Olmo M, Mendes MP. Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods. Sci Total Environ. 2018 May;624:661–72. DOI: https://doi.org/10.1016/j.scitotenv.2017.12.152
Yuxin Z, Shenghong L, Feng J. Overlapping community detection in complex networks using multi-objective evolutionary algorithm. Comput Appl Math. 2017 Mar;36(1):749–68. DOI: https://doi.org/10.1007/s40314-015-0260-1
Sarkar S, Das S, Chaudhuri SS. Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl Soft Comput. 2017 Jan;50:142–57. DOI: https://doi.org/10.1016/j.asoc.2016.10.032
Diao R, Shen Q. Feature selection with harmony search. IEEE Trans Syst Man, Cybern Part B. 2012;42(6):1509–23. DOI: https://doi.org/10.1109/TSMCB.2012.2193613
Yusup N, Zain AM, Latib AA. A review of Harmony Search algorithm-based feature selection method for classification. J Phys Conf Ser. 2019 Mar;1192:12038. DOI: https://doi.org/10.1088/1742-6596/1192/1/012038
Jasim YA, Al-Ani AA, Al-Ani LA. Iris recognition using principal component analysis. Proc - 2018 1st Annu Int Conf Inf Sci AiCIS 2018. 2019;5(5):89–95.
Shrestha A, Mahmood A. Review of Deep Learning Algorithms and Architectures. IEEE Access. 2019;7:53040–65. DOI: https://doi.org/10.1109/ACCESS.2019.2912200
Couronné R, Probst P, Boulesteix A-L. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics. 2018 Dec;19(1):270. DOI: https://doi.org/10.1186/s12859-018-2264-5
Cho G, Yim J, Choi Y, Ko J, Lee S-H. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investig. 2019 Apr;16(4):262–9. DOI: https://doi.org/10.30773/pi.2018.12.21.2
Copyright (c) 2021 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
- Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
- Authors may reproduce or authorize others to reproduce the work or derivative works for the author’s personal use or company use, provided that the source and the copyright notice of Politeknik Elektronika Negeri Surabaya (PENS) publisher are indicated.
- Authors are allowed to use and reuse their articles under the same CC-BY-NC-SA license as third parties.
- Third-parties are allowed to share and adapt the publication work for all non-commercial purposes and if they remix, transform, or build upon the material, they must distribute under the same license as the original.
Plagiarism Check
To avoid plagiarism activities, the manuscript will be checked twice by the Editorial Board of the EMITTER International Journal of Engineering Technology (EMITTER Journal) using iThenticate Plagiarism Checker and the CrossCheck plagiarism screening service. The similarity score of a manuscript has should be less than 25%. The manuscript that plagiarizes another author’s work or author's own will be rejected by EMITTER Journal.
Authors are expected to comply with EMITTER Journal's plagiarism rules by downloading and signing the plagiarism declaration form here and resubmitting the form, along with the copyright transfer form via online submission.
